Module epispot.pre
This module (short for 'pre-compiled') contains models that have
already been compiled and can be put to use immediately. Each function
returns an Model object and its corresponding methods.
Model parameters can still be changed even after compilation, but this
will require recompilation which can be performed with:
Model.compile(custom=False)
# if adding custom compartments:
Model.compile(custom=True)
Expand source code
"""
This module (short for 'pre-compiled') contains models that have
already been compiled and can be put to use immediately. Each function
returns an `epispot.models.Model` object and its corresponding methods.
Model parameters can still be changed even after compilation, but this
will require recompilation which can be performed with:
```python
Model.compile(custom=False)
# if adding custom compartments:
Model.compile(custom=True)
```
"""
from . import comps
from . import models
from . import np
def SIR(R_0, gamma, N):
"""
The well-known
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model);
a staple of epidemiology and the most basic tool for modeling
infectious diseases.
Susceptible → Infected → Removed
## **Parameters**
`R_0`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is. A value of
above 1 indicates a high probability of transmission and
thus an increasing infected population. A value of 1
indicates a low probability of transmission and thus a
constant infected population. A value below 1 indicates
a low probability of transmission and also a decreasing
infected population.
`gamma`: The total recovery rate of patients. This is **not** a
measure of how long it takes patients in any given
compartment to recover but rather a measure of one
divided by the average time of infectiousness.
`N`: The initial population size; should be the same as that
passed into the `epispot.models.Model` class.
## **Returns**
An `epispot.models.Model` object
"""
# compile compartments
Susceptible = comps.Susceptible(R_0, gamma, N)
Infected = comps.Infected()
Removed = comps.Removed()
# compile parameters
if callable(N):
N = N(0)
matrix = np.empty((3, 3), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
matrix[1][2] = (1.0, gamma) # I => R
# compile model
SIR_Model = models.Model(N)
SIR_Model.add(Susceptible, [1], matrix[0])
SIR_Model.add(Infected, [2], matrix[1])
SIR_Model.add(Removed, [], matrix[2])
SIR_Model.compile()
return SIR_Model
def SEIR(R_0, gamma, N, delta):
"""
An extension on the basic
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model)
to include an 'exposed' compartment useful for modeling contact
tracing. This is known as the
[SEIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model)
and is commonly used for diseases that have long incubation periods.
Susceptible → Exposed → Infected → Removed
## **Parameters**
`R_0`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is. A value of
above 1 indicates a high probability of transmission and
thus an increasing infected population. A value of 1
indicates a low probability of transmission and thus a
constant infected population. A value below 1 indicates
a low probability of transmission and also a decreasing
infected population.
`gamma`: The total recovery rate of patients. This is **not** a
measure of how long it takes patients in any given
compartment to recover but rather a measure of one
divided by the average time of infectiousness.
`N`: The initial population size; should be the same as that
passed into the `epispot.models.Model` class.
`delta`: The reciprocal of the incubation period for the disease.
This is one divided by the time it takes an individual to
transition from the `epispot.comps.Exposed` compartment to
the `epispot.comps.Infected` compartment.
## **Returns**
An `epispot.models.Model` object
"""
# compile compartments
Susceptible = comps.Susceptible(R_0, gamma, N)
Exposed = comps.Exposed()
Infected = comps.Infected()
Removed = comps.Removed()
# compile parameters
if callable(N):
N = N(0)
matrix = np.empty((4, 4), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
matrix[1][2] = (1.0, delta) # E => I
matrix[2][3] = (1.0, gamma) # I => R
# compile model
SEIR_Model = models.Model(N)
SEIR_Model.add(Susceptible, [1], matrix[0])
SEIR_Model.add(Exposed, [2], matrix[1])
SEIR_Model.add(Infected, [3], matrix[2])
SEIR_Model.add(Removed, [], matrix[3])
SEIR_Model.compile()
return SEIR_Model
def SIRD(R_0, gamma, N, alpha, rho=1.0):
"""
An addition to the
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model);
which separates the `epispot.comps.Removed` compartment into the
`epispot.comps.Recovered` compartment and the `epispot.comps.Dead`
compartment.
Susceptible → Infected → Recovered, Dead
## **Parameters**
`R_0`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is. A value of
above 1 indicates a high probability of transmission and
thus an increasing infected population. A value of 1
indicates a low probability of transmission and thus a
constant infected population. A value below 1 indicates
a low probability of transmission and also a decreasing
infected population.
`gamma`: The total recovery rate of patients. This is **not** a
measure of how long it takes patients in any given
compartment to recover but rather a measure of one
divided by the average time of infectiousness.
`N`: The initial population size; should be the same as that
passed into the `epispot.models.Model` class.
`alpha`: The probability of death (often referred to as—but not to
be confused with—the 'death rate') for a certain disease.
This is the percentage of individuals that are infected
with the disease that will die.
`rho=1.0`: The reciprocal of the average time before death. This is
the real 'death *rate*' of the disease, which represents
the *time* of death rather than its probability.
## **Returns**
An `epispot.models.Model` object
"""
# compile compartments
Susceptible = comps.Susceptible(R_0, gamma, N)
Infected = comps.Infected()
Recovered = comps.Recovered()
Dead = comps.Dead()
# compile parameters
if callable(N):
N = N(0)
matrix = np.empty((4, 4), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
recovery_rate = (gamma - alpha * rho) / (1 - alpha)
matrix[1][2] = (1.0 - alpha, recovery_rate) # I => R
matrix[1][3] = (alpha, rho) # I => D
# compile model
SIR_Model = models.Model(N)
SIR_Model.add(Susceptible, [1], matrix[0])
SIR_Model.add(Infected, [2, 3], matrix[1])
SIR_Model.add(Recovered, [], matrix[2])
SIR_Model.add(Dead, [], matrix[3])
SIR_Model.compile()
return SIR_ModelFunctions
def SEIR(R_0, gamma, N, delta)-
An extension on the basic SIR Model to include an 'exposed' compartment useful for modeling contact tracing. This is known as the SEIR Model and is commonly used for diseases that have long incubation periods.
Susceptible → Exposed → Infected → Removed
Parameters
R_0: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.N: The initial population size; should be the same as that passed into theModelclass.delta: The reciprocal of the incubation period for the disease. This is one divided by the time it takes an individual to transition from theExposedcompartment to theInfectedcompartment.Returns
An
ModelobjectExpand source code
def SEIR(R_0, gamma, N, delta): """ An extension on the basic [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model) to include an 'exposed' compartment useful for modeling contact tracing. This is known as the [SEIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model) and is commonly used for diseases that have long incubation periods. Susceptible → Exposed → Infected → Removed ## **Parameters** `R_0`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma`: The total recovery rate of patients. This is **not** a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `N`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. `delta`: The reciprocal of the incubation period for the disease. This is one divided by the time it takes an individual to transition from the `epispot.comps.Exposed` compartment to the `epispot.comps.Infected` compartment. ## **Returns** An `epispot.models.Model` object """ # compile compartments Susceptible = comps.Susceptible(R_0, gamma, N) Exposed = comps.Exposed() Infected = comps.Infected() Removed = comps.Removed() # compile parameters if callable(N): N = N(0) matrix = np.empty((4, 4), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate matrix[1][2] = (1.0, delta) # E => I matrix[2][3] = (1.0, gamma) # I => R # compile model SEIR_Model = models.Model(N) SEIR_Model.add(Susceptible, [1], matrix[0]) SEIR_Model.add(Exposed, [2], matrix[1]) SEIR_Model.add(Infected, [3], matrix[2]) SEIR_Model.add(Removed, [], matrix[3]) SEIR_Model.compile() return SEIR_Model def SIR(R_0, gamma, N)-
The well-known SIR Model; a staple of epidemiology and the most basic tool for modeling infectious diseases.
Susceptible → Infected → Removed
Parameters
R_0: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.N: The initial population size; should be the same as that passed into theModelclass.Returns
An
ModelobjectExpand source code
def SIR(R_0, gamma, N): """ The well-known [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model); a staple of epidemiology and the most basic tool for modeling infectious diseases. Susceptible → Infected → Removed ## **Parameters** `R_0`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma`: The total recovery rate of patients. This is **not** a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `N`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. ## **Returns** An `epispot.models.Model` object """ # compile compartments Susceptible = comps.Susceptible(R_0, gamma, N) Infected = comps.Infected() Removed = comps.Removed() # compile parameters if callable(N): N = N(0) matrix = np.empty((3, 3), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate matrix[1][2] = (1.0, gamma) # I => R # compile model SIR_Model = models.Model(N) SIR_Model.add(Susceptible, [1], matrix[0]) SIR_Model.add(Infected, [2], matrix[1]) SIR_Model.add(Removed, [], matrix[2]) SIR_Model.compile() return SIR_Model def SIRD(R_0, gamma, N, alpha, rho=1.0)-
An addition to the SIR Model; which separates the
Removedcompartment into theRecoveredcompartment and theDeadcompartment.Susceptible → Infected → Recovered, Dead
Parameters
R_0: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.N: The initial population size; should be the same as that passed into theModelclass.alpha: The probability of death (often referred to as—but not to be confused with—the 'death rate') for a certain disease. This is the percentage of individuals that are infected with the disease that will die.rho=1.0: The reciprocal of the average time before death. This is the real 'death rate' of the disease, which represents the time of death rather than its probability.Returns
An
ModelobjectExpand source code
def SIRD(R_0, gamma, N, alpha, rho=1.0): """ An addition to the [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model); which separates the `epispot.comps.Removed` compartment into the `epispot.comps.Recovered` compartment and the `epispot.comps.Dead` compartment. Susceptible → Infected → Recovered, Dead ## **Parameters** `R_0`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma`: The total recovery rate of patients. This is **not** a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `N`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. `alpha`: The probability of death (often referred to as—but not to be confused with—the 'death rate') for a certain disease. This is the percentage of individuals that are infected with the disease that will die. `rho=1.0`: The reciprocal of the average time before death. This is the real 'death *rate*' of the disease, which represents the *time* of death rather than its probability. ## **Returns** An `epispot.models.Model` object """ # compile compartments Susceptible = comps.Susceptible(R_0, gamma, N) Infected = comps.Infected() Recovered = comps.Recovered() Dead = comps.Dead() # compile parameters if callable(N): N = N(0) matrix = np.empty((4, 4), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate recovery_rate = (gamma - alpha * rho) / (1 - alpha) matrix[1][2] = (1.0 - alpha, recovery_rate) # I => R matrix[1][3] = (alpha, rho) # I => D # compile model SIR_Model = models.Model(N) SIR_Model.add(Susceptible, [1], matrix[0]) SIR_Model.add(Infected, [2, 3], matrix[1]) SIR_Model.add(Recovered, [], matrix[2]) SIR_Model.add(Dead, [], matrix[3]) SIR_Model.compile() return SIR_Model